Perceived Organizational Support Predicts Emotional Labor Among Nurses
Bibliographic record
Abstract
Background: Nursing is among the various occupations that require management of emotions according to the job demands. Emotional labor and lack of reward are the main sources of mental health outcomes among the nurses. It is very important that more researches, which contemplate the emotional labor importance and unfavorable mental health effects, be carried out. Aim: This study was aimed to investigate the effect of perceived organizational Support on emotional labor among nurses. Method: The present study was a correlational study, consist of 200 nurses both Male nurses (N=100) and Females nurses (N=100), from different hospitals and clinics. Age ranged from 20 to 51 years (M= 30.50; S.D= 4.40). Data of the study was collected through convenient sampling technique. Participants were assessed by Shorten Version of Survey of Perceived Organizational Support (Eisenberger et al, 1986) in order to measure perceived organizational support and Dutch Questionnaire on Emotional Labor (D-QEL) (Näring, Briët, & Brouwers, 2007) in order to assess emotional labor. Results: The results revealed that perceived organizational support significantly predicts emotional labor. By improving the perception of organizational support among nurses, the experience of emotional labor can be reduced. Conclusion: The purpose of the present study was to develop a health-care model of emotional labor which could help the organizations to understand the role of perceived organizational support on the reactions to the strain of the emotional labor. The present study revealed that perceived organizational support is a significant predictor of emotional labor. Informal types of organizational support (e.g., a perception that the organization is concerned with one's personal life) are important for expatriate success, and should be incorporated into expatriate programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".